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Modelling and forecasting Australian domestic tourism

Author

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  • George Athanasopoulos

    ()

  • Rob J. Hyndman

    ()

Abstract

In this paper, we model and forecast Australian domestic tourism demand. We use a regression framework to estimate important economic relationships for domestic tourism demand. We also identify the impact of world events such as the 2000 Sydney Olympics and the 2002 Bali bombings on Australian domestic tourism. To explore the time series nature of the data, we use innovation state space models to forecast the domestic tourism demand. Combining these two frameworks, we build innovation state space models with exogenous variables. These models are able to capture the time series dynamics in the data, as well as economic and other relationships. We show that these models outperform alternative approaches for short-term forecasting and also produce sensible long-term forecasts. The forecasts are compared with the official Australian government forecasts, which are found to be more optimistic than our forecasts.

Suggested Citation

  • George Athanasopoulos & Rob J. Hyndman, 2006. "Modelling and forecasting Australian domestic tourism," Monash Econometrics and Business Statistics Working Papers 19/06, Monash University, Department of Econometrics and Business Statistics.
  • Handle: RePEc:msh:ebswps:2006-19
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    File URL: http://www.buseco.monash.edu.au/ebs/pubs/wpapers/2006/wp19-06.pdf
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    References listed on IDEAS

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    11. Ashton de Silva & Rob J. Hyndman & Ralph D. Snyder, 2007. "The vector innovation structural time series framework: a simple approach to multivariate forecasting," Monash Econometrics and Business Statistics Working Papers 3/07, Monash University, Department of Econometrics and Business Statistics.
    12. J Keith Ord & Ralph D Snyder & Anne B Koehler & Rob J Hyndman & Mark Leeds, 2005. "Time Series Forecasting: The Case for the Single Source of Error State Space," Monash Econometrics and Business Statistics Working Papers 7/05, Monash University, Department of Econometrics and Business Statistics.
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    Citations

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    Cited by:

    1. OROIAN, Maria & RATIU, Ramona-Flavia & GHERES, Marinela, 2013. "Using The Residents’ Profile As Potential Tourists In Tourist Market Segmentation: The Case Of Mures County, Romania," Academica Science Journal, Economica Series, Dimitrie Cantemir University, Faculty of Economical Science, vol. 1(2), pages 21-34, May.
    2. Deng, Minfeng & Athanasopoulos, George, 2011. "Modelling Australian domestic and international inbound travel: a spatial–temporal approach," Tourism Management, Elsevier, vol. 32(5), pages 1075-1084.
    3. Tamara Mata & Carlos Llano, 2013. "Social networks and trade of services: modelling interregional flows with spatial and network autocorrelation effects," Journal of Geographical Systems, Springer, vol. 15(3), pages 319-367, July.
    4. Athanasopoulos, George & Ahmed, Roman A. & Hyndman, Rob J., 2009. "Hierarchical forecasts for Australian domestic tourism," International Journal of Forecasting, Elsevier, vol. 25(1), pages 146-166.
    5. Bermúdez, José D. & Corberán-Vallet, Ana & Vercher, Enriqueta, 2009. "Multivariate exponential smoothing: A Bayesian forecast approach based on simulation," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(5), pages 1761-1769.
    6. Amira Gasmi & Seifallah Sassi, 2015. "International tourism demand in Tunisia: Evidence from dynamic panel data model," Economics Bulletin, AccessEcon, vol. 35(1), pages 507-518.
    7. Andrawis, Robert R. & Atiya, Amir F. & El-Shishiny, Hisham, 2011. "Combination of long term and short term forecasts, with application to tourism demand forecasting," International Journal of Forecasting, Elsevier, vol. 27(3), pages 870-886, July.
    8. Ahmad Farid Osman & Maxwell L. King, 2015. "A new approach to forecasting based on exponential smoothing with independent regressors," Monash Econometrics and Business Statistics Working Papers 2/15, Monash University, Department of Econometrics and Business Statistics.

    More about this item

    Keywords

    Australia; domestic tourism; exponential smoothing; forecasting; innovation state space models.;

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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